I think part of why this is hard to believe is that people strongly believe in the concept that time is money. On the margins for decisions like hiring someone to mow your lawn, it is true. For large scale things, you often cannot accelerate processes no matter how much money you dump into it. A good example of this is how long it has taken China to industrialize.

To be clear also, you have to prove your point that #2 is outpacing #1. The fact that the price keeps going up is not proof as there are other explanations. The poor quality of domestic manufacturers and their bad business practices, for example.

You’re not, though, because that truck never did and never could exist. A modern F-150 isn’t a 70s F1 car made cheap by new tech. This isn’t something you can wave away with an argument equivalent to “we put 1000 research points in the tech tree.”

When the US economy was working well, products got better and cheaper over time. Tech and increased labor productivity drove that. Now, tech and labor productivity has continued to increase, yet consumer prices have far outpaced inflation.

Why not do the obvious comparison with terrestrial data centers?

There’s some truly magical thinking behind the idea that government regulations have somehow made it cheaper to launch a rocket than build a building. Rockets are fantastically expensive even with the major leaps SpaceX made and will be even with Starship. Everything about a space launch is expensive, dangerous, and highly regulated. Your datacenter on Earth can’t go boom.

This looks crazy because it is incorrect. In your premise, that 9% profit margin includes the regulatory costs for a brick and mortar restaurant already. The only way your logic works out is if truck regulations are on average $30k more expensive than a regular building, which they almost certainly are not.

You can’t even begin to do the calculation without knowing the breakdown underlying the profit margin you cite.

In my experience writing computer vision software, people really struggle with the common sense of how fast computers really are. Some knowledge like how many nanoseconds an add takes can be very illuminating to understand whether their algorithm's runtime makes any sense. That may push loose the bit of common sense that their algorithm is somehow wrong. Often I see people fail to put bounds on their expectations. Numbers like these help set those bounds.

Many problems can performantly solved in pure Python, especially via the growing set of tools like the JIT libraries I cited. Even more will be solvable when things like free threaded Python land. It will be a minority of problems that can’t be, if it isn’t already.