Abstraction Equivocation

During my outreach, onboarding, and lobbying for PauseAI, there’s a pattern of mistaken reasoning I see repeatedly, which I expect to become higher stakes with Senators Sanders and Casar’s recent proposal to ban superintelligence.  I’m going to call it Abstraction Equivocation, which is applying implementation or trust level critiques to directional proposals. Here’s what I mean by these terms: Directional: about priorities, goals, and central assumptions.  As examples of priorities: preventing x-risk from AI is non-negotiable and required for anything else to matter; competitiveness is a constraint other values need to work within to not undercut themselves; equity, innovation, and stability trade off with each other in context-dependent ways, with room for legitimate disagreement as to the proper balance.  As examples of central assumptions: superintelligent AI is a meaningful concept, likely to occur in a relevant timeframe, and extremely dangerous if not handled well.  Directional critiques should engage with whether these are the right priorities and if their foundational assumptions make sense. Implementation: about specific mechanisms.  Does a given definition of superintelligence capture what is intended, exclude what is not, and is there a clear way to tell?  Will a given monitoring system catch what it needs to catch?  Such questions require a clear point of reference. Trust: about who decides the details.  Can a given agency be trusted to make good decisions without getting captured?  Can the courts be trusted to interpret key definitions correctly?  Trust sits at the bridge between direction and implementation, applying to proposals that are directional, but with a plan to fill in the details, assessing the people who are responsible for that filling in. These levels are relative and recursive.  International coordination, for example, is directional relative to the monitoring and enforcement regimes applied, and also an implementation of preventing x-risk.  High level proposals for monitoring, in turn, are implementation details of coordination, and also directional in the sense that they need to be implemented on a technical level--at the direction of specific agencies, which require trust. Here are some common examples of what I consider Abstraction Equivocation: The US can’t slow down AI development or else China will race ahead Assumes that a hypothetical agreement will not contain adequate monitoring and enforcement provisions to prevent exactly this from happening.  You have to actually look at the relevant provisions to know if they are inadequate. Regulating AI will lock-in a monopoly for industry incumbents Assumes that hypothetical regulation will introduce extensive bureaucratic red tape that applies equally to everyone, creating a high-cost barrier to entry.  In reality, actual proposals where this objection tends to be raised (1) focus on frontier development, which is vastly more gated by compute and infrastructure costs, such that regulation is a drop in the bucket, and (2) have cost thresholds below which the regulation does not apply--and sometimes even specific carveouts for open source. Or to put it in simpler terms: RTFB or GTFO. In contrast, here are some example objections that are based on the same worldviews as the above, but don’t involve Abstraction Equivocation: Directional: Concerns about “x-risk” are overblown.  AI alignment, to the extent it was ever a meaningful concept, is basically solved.  We should prioritize innovation so that we get the benefits of AI as soon as possible. Implementation: “Superintelligence” is poorly defined in this bill and could be reasonably interpreted to include all sorts of normal technologies. Trust: Once you create any sort of government agency to regulate AI, it’s going to be subject to perverse incentives to drift from its original purpose and centralize power in the national government or create regulatory moats for industry incumbents. These examples vary widely in how wrong I believe they are, but each at least clearly points toward what kind of response it invites. Spotting Abstraction Equivocation in the wild can be tricky, because directional-sounding statements can imply expectations about implementation.  Consider this recent X post from Dwarkesh Patel (which I'm picking on because it's subtle not because it's egregious): Notice that it never actually quotes, paraphrases, or summarizes any provisions in any particular piece of legislation (such as the Sanders-Casar bill) that are inadequate, underspecified, or likely to lead to unintended consequences.  For example, the argument that: “During a pause of AI development, compute would continue to build up” assumes that the bill doesn’t contain adequate compute tracking and governance.  Maybe it doesn’t.  Maybe its attempts are underspecified.  Maybe the details have been delegated to an agency that lacks sufficient authority, competence, or trustworthiness.  All of these are