Your post may apply if we're working with 4K+ resolution video files, rendering and other activities, but the modern programmer, even compiling binaries, will be fine on a MacBook Air.
How do I know?
I use a 2 generation old Macbook as my primary personal development machine. I write Go, Rust, Java and TypeScript using the common tool chains for all those languages.
A perfect example of this the recent Level1Techs video about compiling an Unreal engine game using the AMD Threadripper 3990X[1]. Some concrete advice:
1. Often the heaviest aspect of a programmer's workflow isn't compiling the code, but running automated tests using VMs and stuff
2. compilers tend to benefit from large cache sizes and good single-threaded performance. Multi-threaded performance only makes a difference in limited scenarios.
I've also heard that storage performance, and in particular storage latency, can make a big difference on compiler performance, but the difference is starker on Windows than on Linux because NTFS is a lot slower than many Linux file systems (EXT4, XFS, etc.).
The thing is, we as programmers don't have a good mental model for how the software tools we use (IDEs, virtualization/docker, compilers) scale with modern hardware, and very few technology reviewers are producing content that would help us understand.
Add some server processes, UI watchers/builders, and my Thinkpad starts to freeze up. In an ideal world, I would have a low-latency remote desktop going, but even in 2020 it seems like a pipe dream.
5ms encoding lag, 10ms network lag, totally workable.
I wrote a little about this; it's for Rails but the same ideas apply:
https://medium.com/@bdcravens/fixing-docker-for-mac-and-rail...
On the flip side standard issue laptops are now 16" Macbook Pro's with 32GB of RAM which is pretty hardware.
I don't see Airs as development platforms. They are portable platforms.
Not to mention previous MacBook Air were Dual Core only.
A lot of usecases involve running a replication of the server environment locally in resource intensive containers
I'm surprised no one else operates this way.
The performance gains are immense.
It's immensely flexible and I'm surprised it's not more common.
Also, a cloud VM, if it breaks, is replaced "for free" and "instantly"; fixing a physical machine has a cost and is a delay. Unless you're big enough to keep a spare (probably makes sense for many dozens of machines), it also looks a bit uneconomical from the business's POV.
Never mind that your cloud costs in a year end up being comparable to said workstation.
That's why I'm tempted to build a custom workstation at home: so that I always have access to the compute resources I need to do my job.
A typical compile for me takes about twice as long compared to colleagues with newer H-series processors, but the individual projects are pretty small so that often isn't a huge deal.
However, once you try and run nearly a dozen docker containers for local testing, things slow down quite a bit. Multitasking while those tests are running is very difficult.