AI Policymakers are responsible for delivering effective governance mechanisms that can provide safe, aligned and trustworthy AI development. However, the information environment offered to policymakers is characterized by an unnecessarily low signal-to-noise ratio, favouring regulatory capture and creating deep uncertainty and divides on which risks should be prioritized from a governance perspective. We posit that the current speed of publication in AI combined with the lack of strong scientific standards, via weak reproducibility protocols, effectively erodes the power of policymakers to enact meaningful policy and governance protocols. Our paper outlines how AI research could adopt stricter reproducibility guidelines to assist governance endeavours and improve consensus on the risk landscapes posed by AI. We evaluate the forthcoming reproducibility crisis within AI research through the lens of reproducibility crises in other scientific domains and provide a commentary on how adopting preregistration, increased statistical power and negative result publication reproducibility protocols can enable effective AI governance. While we maintain that AI governance must be reactive due to AI's significant societal implications we argue that policymakers and governments must consider reproducibility protocols as a core tool in the governance arsenal and demand higher standards for AI research.