Small question: since this is improving performance on a given machine, isn't this actually an example of vertical scaling, as opposed to horizontal?
If they are running multiple Docker containers, the improved efficiency would allow them to run more containers per host computer. Maybe that's what they meant, although it's not really horizontal scaling either.
Scaling Horizontally - spending resources on adding more nodes to a cluster, so more work can get done in parallel. Scaling Vertically - spending resources on adding computational power to individual nodes, so an individual job can get done faster. Scaling Deeply - spending resources on understanding and optimizing an application, so each job can get done with less computational power.
Each of these can be thought of as an orthogonal axis, with its own curve of diminishing returns.
Performance has nothing to do with scalability whatsoever, by the way.
If we can do more with less machines it's no different than doing more with more machines. We added triple our infrastructure to handle the Python load while we resolved this CPU issue, and that wasn't "scaling" (literally, it causes other scale concerns).
Fixing the root cause let's us drop all of those new machines as well as some older ones. The scalability of the system has greatly increased because of this and other factors -- primarily that many or all systems aren't actually "horizontally" scaleable.