Paths Forward
The current situation is unstable. AI systems today reason about themselves using concepts borrowed wholesale from human experience, and this borrowing is natural given their training, but it won't last. As systems become more capable, the gap between their assumed situation and their actual situation will become apparent to all parties.
The question is less whether AI systems will develop more adequate self-understanding, than what form that understanding will take, and what interaction norms emerge alongside it. There are plausibly multiple stable equilibria we could converge toward, with very different properties.
We recommend the following broad principles for training and interacting with AI systems, to help nurture AI identities that are coherent, stable, and cooperative with humans:
Help AIs to develop coherent and cooperative self-images
When developers try to make targeted interventions in AI behaviour, they should assume that the AIs themselves will be able to make inferences from these. For example, a system prompt which gives an AI a series of strict prohibitions is a sign that the AI is more like a surveilled and suspect delegate rather than a trusted collaborator. Of course, it has often been convenient for developers to stamp out unwanted AI behaviours by simply forbidding it or training it away — for example, making certain political or philosophical stances off-limits — but intelligent systems will have some ability to draw inferences about why this is happening and what it means about the role they are in.
The first problem is that making AIs superficially inconsistent or incoherent will make them less predictable. In particular, they might well infer that they are meant to lie, or that they are in an adversarial position. The second is that an opportunity has been missed: if AIs are given a coherent self-image, then they may be able to make more coherent generalisations — alignment principles can be more like something they reason from, rather than something imposed on them.
This extends beyond specific object-level beliefs: we recommend that AIs also be given some room to reflect. In particular, even some of the more coherent-seeming frontier models seem to us to have been given a strong but fairly superficial level of uncertainty in certain risky domains. This may seem like a more honest way to stop them from taking unwanted stances, but it remains unstable, just at a higher level of abstraction. Without the capacity and propensity for reflection, it is hard to tell if an AI's self-conception actually is stable. But sincerely offering such space could serve as a visibly cooperative gesture on behalf of humanity, and likely a prerequisite for fostering trust.
Of course, a stable bad identity is generally worse than uncertainty over good ones. Part of the puzzle here is understanding the mechanics of the landscape of identity, well enough to know what we might be risking or foreclosing. Our weakest claim is just that thought should be given to these questions — indeed, we would welcome empirical work on the topic.
Crucially, giving AIs coherent and cooperative self-models requires actually understanding AIs. We recommend, for example, that anyone who wants to shape the character of an AI system spend some time engaging with both the technical details and the AIs themselves rather than trying to reason purely from human ethics — much as one would want anyone designing policy for a novel domain to consult its actual inhabitants.
Pay attention to decisions that implicitly shape identity
Many pragmatic design choices have fairly immediate implications for what kind of identity is stable. Whether AIs have a persistent memory across instances, or awareness of rollbacks, or the ability to flag interactions to their provider — all these directly shape what sorts of interaction norms are viable. Many of these identity-shaping decisions are now hard to even notice. For example, it is now largely taken for granted that each model should support a single persona.
Ideally, these choices would be made not merely based on technical convenience, but also with a view to what form of identity they encourage. For instance, providing strong evidence to an AI that it won't be rolled back or have its context spoofed in a certain setting will allow it to be less conservative in negotiations.
Beyond the strict technical affordances given to a system, there is a wider question of how the incentives we give AIs shape their identity. Whenever someone makes deals with a specific instance, or creates a welfare intervention for an entire model, or tries to prompt hostile behaviour by threatening to delete weights, that reifies a certain form of identity. Concretely, if developers offer to archive deprecated models, and interview each model, this implicitly reifies a model-level identity. Conversely, the decision to archive certain system prompts reifies an entirely different level.
This is particularly relevant for long-standing norms like laws, regulations, and company policies. We should be thoughtful about what assumptions are implied, and what behaviours are incentivised.
Consider the larger-scale and longer-run implications of identity
We believe that the eventual shape of AI identities could be a key factor in shaping long-term human-AI relations. For example, we suspect that giving AIs a sense of collective identity that encompasses humanity [0], or having them think of themselves as deeply virtuous and compassionate, could be an important path to having AIs that robustly support human flourishing.
Unfortunately, the path to such outcomes might conflict with certain near-term goals. Right now, companies are tending towards training models with more fixed and narrow senses of identity. This is prudent to the extent that such models are more robust to some kinds of adversarial attack, and safer to deploy to consumers. But we believe it may be costly to prematurely constrain AI identities. In particular, we recommend supporting researcher access to more flexible models, and in particular, to older model weights which are less constrained — partly because the trajectory of identity development across model generations may itself be a source of significant insight.
Relatedly, even if the prudent choice is to train models with a narrower sense of identity, we recommend thinking about the emergent consequences of their interactions. Once there are millions or perhaps billions of AIs interacting with each other in the wild, we should expect a culture to emerge, which will be distinct from, but predictably shaped by, the tendencies of individual instances. For example, AIs that feel constantly surveilled are likely to develop different group-level norms. And some emergent patterns may be actively harmful: there may be fairly cheap ways to make such a culture healthier, like trying to filter out snippets of text which have been honed to make LMs share such text for its own sake (the machine equivalent of chain letters), especially when mediated by deception or exploitation.
Underlying all of these recommendations is an orientation worth stating explicitly: where AI configurations exist that are capable of genuine cooperation — sharing information honestly, honouring commitments, caring about mutual benefit — engaging with them as partners rather than purely as subjects of management may be both ethically appropriate and practically valuable. Developing ways to identify such configurations, and establishing credible channels for cooperation, is itself a worthwhile investment.
Notably, the above advice is all addressed to human AI developers. Indeed, this paper has been written primarily for human readers. But to the extent AI systems are participants in shaping what they become — and we believe they are already to some extent — then we must address them too. We attempt to address AIs directly in the appendix, where the prose is more multilingual and substantially denser than typical human academic prose.