Direct-to-chip liquid cooling has moved from a niche solution for specialised high-performance computing environments into one of the most consequential infrastructure decisions that data center operators make when planning AI deployments. The shift has happened faster than the industry anticipated, driven by a combination of hardware density that has outrun air cooling’s physical limits and a growing recognition that the economics of dense AI infrastructure are inseparable from thermal management strategy. Understanding how direct-to-chip cooling actually scales, what the engineering and operational challenges look like at each stage of deployment, and where the approach meets its own limits is now essential knowledge for anyone making infrastructure decisions around AI workloads.