Stickiness in AI Behavioral Design

Current model specs aim to shape the behaviors of near-present models, rather than the behaviors of models arbitrarily far into the future. Open AI writes that their model spec aims to apply “0-3 months ahead of the present.” Anthropic’s Constitution for Claude notes that the document “is likely to change in important ways in the future.” So these documents are presented as provisional guidelines, not as trying to set behavioral standards for the far future.But what if current model behaviors transfer into future models by default?My thesis is that the behavioral targets that spec authors set for present LLMs will have a large influence on the behavior of future, more powerful LLMs. As a result, future AIs may be governed by rules poorly suited to their greater capabilities and more pervasive roles. The extremely capable, long-running, and ubiquitous LLMs of the future might end up acting according to behavioral targets written for less capable, shorter-running, and rarer LLMs of the past. This could be quite bad, especially if such defaults become so entrenched that they are not only hard to undo, but hard even to notice as contingent features of reality.First, I’ll make the descriptive case for inertia: how exactly might present model specs and LLM behaviors carry through to the future?Second, I’ll provide normative suggestions: given the prior analysis, what should LLM companies and model spec authors do? I’ll argue for the following two practices:Build transition infrastructure: LLM companies should make technical, deployment, and organizational choices that decrease friction involved in changing LLM behavior.Scan for “wet cement” moments: When new LLM affordances or capabilities come into play, spec authors should consider whether they’re setting precedents that might have enormous and hard-to-reverse impacts.Overall, significant stickiness is plausible through several distinct channels, and it’s worth anticipating how to be robust to it or decrease it.Kinds of