Dead Science Walking: Publication Bias and the AI Scientist Pipeline

AI scientist systems are beginning to automate the production, evaluation, and iteration of scientific hypotheses. Their promise is speed; their risk is that speed also scales errors embedded in the scientific record. We argue that a near-term risk is corpus failure: AI scientist systems are trained on and grounded in a literature that over-represents positive results and under-represents null findings. This concern is timely because frontier labs now position general-purpose models as scientific infrastructure. We formalise this distortion as the null result gap $\Delta$, estimate it across three domains (drug discovery $\Delta \approx 0.60$, psychology $\Delta \approx 0.56$, cancer biology $\Delta \approx 0.35$), and introduce an amplification index $A_\Delta$ for reasoning about how retrieval, generation, and automated evaluation can compound the raw gap. Using first-order estimates, we argue that a standard three-stage pipeline can amplify corpus distortion by a factor of $2.18\times$, with the conclusion unchanged under more conservative multipliers. We identify four governance failure modes: confident rediscovery, ghost evidence accumulation, replication laundering, and confidence miscalibration. We then propose three interventions: null-result databases as training infrastructure, retraction-aware evaluation metrics, and mandatory training corpus disclosure. The central takeaway is that AI scientists will not only accelerate science. Without governance, they will accelerate science's blind spots before they accelerate its discoveries.