Virtual Machines (VMs) with Just-In-Time (JIT) compilers are traditionally thought to execute programs in two phases: first the warmup phase determines which parts of a program would most benefit from dynamic compilation, before JIT compiling them into machine code; after compilation has occurred, the program is said to be at a steady state of peak performance. When measuring the performance of JIT compiling VMs, data collected during the warmup phase is generally discarded, placing the focus on peak performance. We introduce a fully automated statistical approach, based on changepoint analysis, which allows us to determine if a program has reached a steady state and, if so, whether that represents peak performance or not. Using this, we show that even when run in the most controlled of circumstances, small, deterministic, widely studied microbenchmarks often fail to reach a steady state of peak performance on a variety of common VMs. Repeating our experiment on 3 different machines, we found that at most 43.5% of <VM, benchmark> pairs consistently reach a steady state of peak performance.