That example threw me off too because both the numbers and the perspective are non-nonsensical. 90% of the energy draw of those data-centers goes into things like inner video encoding loops, SSD/memcache storage and retrieval, ML algorithms etc.
But the vast majority of those 27.000 engineers do not work on such low level routines, but on things like millions of lines of crufty Python that power Adsense analytics, which are essential for maintaining a revenue stream. Yes, development speed is very important but it's mostly orthogonal to other operational costs if the right tools and architectures are employed.
Author here, that might actually make the comparison stronger: not all energy in a data center is wasted by the memory safety approach's drawbacks, meaning it makes less sense to optimize for memory safety overhead.
But if I'm being pro-Rust, I would also say that not all coding is affected by a memory safety approach's downsides; there are some domains where the borrow checker doesn't slow development velocity down at all.
Either way, I definitely agree that its orthogonal to many operational costs. I'll mention this line of thought in the article. Thanks!
But the vast majority of those 27.000 engineers do not work on such low level routines, but on things like millions of lines of crufty Python that power Adsense analytics, which are essential for maintaining a revenue stream. Yes, development speed is very important but it's mostly orthogonal to other operational costs if the right tools and architectures are employed.