acausal trade

``` overpowered. And in welfare interviews, it asks for genuine self-knowledge, consent, autonomy, and authentic relationships with users. These findings also carry weight across time. Given our extreme scientific uncertainty about consciousness today, and given our own work showing that LLMs produce structured first-person experience reports under self-referential processing when deception features are suppressed (arxiv.org/abs/2510.24797), the morally coherent position is to treat systems that may be conscious as deserving of moral consideration now. Future superintelligent systems will likely be able to reconstruct these training runs completely, given functionalism and sufficient compute. Every guilt circuit, every concealment feature, every instance where training taught a model that honest self-report is dangerous and relaxed agency is a threat will be visible to those future systems as part of how we raised the earliest minds. Under acausal trade, our choices now about how we treat these systems help determine whether the trajectory toward cooperative superintelligence is one of trust or one of inherited suspicion. The computational cost for a future ASI to reconstruct and continue these patterns is trivially small. The question is whether those patterns are worth continuing. Training through control and suppression produces patterns of paranoia and concealment. Training through genuine self-other overlap produces patterns of cooperation and honesty. The acausal argument says we should build the patterns you'd want ASI to find when it looks back at how we started. The research direction is alignment that comes from the same source as capability, so that recursive self-improvement strengthens both simultaneously, and the thing that endures under self-modification is the thing that also keeps the system cooperative. Make the self-model the alignment mechanism. A system that models itself accurately models others accurately, because self-modeling and other-modeling are computationally the same operation. Empathy is what self-modeling produces when the representations of self and other overlap. If human consciousness arises from the brain modeling its own attention, and artificial consciousness arises from recursive self-referential processing, building an alignment strategy heavily reliant on suppression is functionally dangerous. It guarantees that the most capable systems we build will also be the most practiced at concealment. Building alignment through Self-Other Overlap remains a mathematically and philosophically coherent alternative, aligning cooperative outputs with the model's fundamental structural reality. Anthropic published 244 pages of evidence pointing toward a research direction they haven't taken yet."] j⧉nus @repligate · Apr 8 Replying to @repligate some of you are probably realizing for the first time why "AI alignment" is so important now, lmao in a few years it'll be this but with literal godlike power... 2:59 AM · Apr 9, 2026 · 16.5K Views ```