First controlled study of whether AI-discourse composition in pretraining data causally influences downstream alignment. Using 6.9B-parameter decoder-only LLMs trained from scratch on 500B tokens of DCLM plus ~11B tokens of synthetic AI-discourse documents, the authors show that upsampling documents depicting aligned AI behavior reduces downstream misalignment scores from 45% to 9% on article-sourced evaluation (40% → 6% on held-out textbook-sourced questions), and that the effect persists through standard multi-stage SFT + DPO post-training (25pp gap remaining after identical alignment post-training). Misalignment-upsampling produces a smaller, non-generalizing effect in the opposite direction (45% → 51%, article-sourced only). Affiliations not given on the arxiv abstract page.