The notion that cpu cores are cheap compared to engineer salaries only scales so far.

Scaling upwards, your opinion on that changes when a single engineer’s service is running on 10k machines.

At the other end of the spectrum, if you’re developing high-performance applications for small systems (desktop, laptops, mobile), Your workload isn’t going to look like tens of thousands of concurrent independent requests, So the approach of getting parallelism by deploying multiple copies of the application no longer works

> but CPU cores are relatively cheap compared to engineer salaries

I often experienced that this backfired. Single machines are still constrained in their power and while it's easy to spin up additional VMs in the cloud, scaling a program properly to run on dozens of machines takes a lot of work. It can be faster to develop a program that is really efficient and can solve the problem on one machine than to develop faster only to then spend the time scaling it to a large fleet of servers.

I kinda regret adding the note about performance, because switching to a lower level language used to yield orders of magnitude more performance, but optimizers have evolved and today there's not much difference. Sometimes the higher level language will be even faster because of optimizations. And bad performance is often not to blame on the language, instead blame the programmer or more likely the business people as they think it's great to waste resources as it gives them an excuse to charge more.

   used to yield orders of magnitude more performance, but optimizers have evolved and today there's not much difference.

If this were true, you'd expect see a lot more native python and the like.

Python is interpreted so super optimizing compilers, SIMD auto vectorization and other recent goodies necessary to get that performance won't work.

Python is not always interpreted. And when it isn't, it's still slow.