According to @JeffDean, Google Research highlighted a machine-learning approach that predicts virtual machine lifetimes to optimize VM placement in real-world systems. Source: Jeff Dean on X https://twitter.com/JeffDean/status/1979278480316469718 and Google Research on X https://x.com/GoogleResearch/status/1979260959286853693 The post provides no quantitative metrics, deployment timeline, or verified cost or utilization impacts, so there are no measurable efficiency gains to price today. Source: Jeff Dean on X https://twitter.com/JeffDean/status/1979278480316469718 For traders, the actionable step is to monitor for an official Google Research paper or benchmark that quantifies scheduling accuracy and placement efficiency before adjusting positions in AI infrastructure themes. Source: Google Research on X https://x.com/GoogleResearch/status/1979260959286853693 For crypto markets, any validated cloud scheduling efficiency data could influence narratives around centralized versus decentralized compute; monitor sector reactions only after metrics are public. Source: Jeff Dean on X https://twitter.com/JeffDean/status/1979278480316469718 and Google Research on X https://x.com/GoogleResearch/status/1979260959286853693