The script doesn’t go away when you replace a helpdesk operator with ChatGPT. You just get a script-reading interface without empathy and a severally hindered ability to process novel issues outside it’s protocol.

The humans you speak to could do exactly what you’re asking for, if the business did not handcuff them to a script.

The reason the article compares to commercial flights is your everyday reader knows planes’ emissions are large. It’s a reference point so people can weight the ecological tradeoff.

“I can emit this much by either (1) operating the global airline network, or (2) running cloud/LLMs.” It’s a good way to visualize the cost of cloud systems without just citing tons-of-CO2/yr.

Downplaying that by insisting we look at the transportation industry as a whole doesn’t strike you as… a little silly? We know transport is expensive; It is moving tons of mass over hundreds of miles. The fact computer systems even get close is an indication of the sheer scale of energy being poured into them.

Amdahl’s isn’t the only scaling law in the books.

Gustafson’s scaling law looks at how the hypothetical maximum work a computer could perform scales with parallelism—idea being for certain tasks like simulations (or, to your point, even consumer devices to some extent) which can scale to fully utilize, this is a real improvement.

Amdahl’s takes a fixed program, considers what portion is parallelizable, and tells you the speed up from additional parallelism in your hardware.

One tells you how much a processor might do, the only tells you how fast a program might run. Neither is wrong, but both are incomplete picture of the colloquial “performance” of a modern device.

Amdahl’s is the one you find emphasized by a Comp Arch 101 course, because it corrects the intuitive error of assuming you can double the cores and get half the runtime. I only encountered Gustafson’s law in a high performance architecture course, and it really only holds for certain types of workloads.