This makes sense if and only if you entirely ignore all secondary and tertiary effects of your choices.

Things like massively increased energy cost, strain on the grid, depriving local citizens of resources for your datacenter, and let's not forget ewaste, pollution from higher energy use, pollution caused by manufacturing more and more chips, pollution and cost of shipping more and more chips across the planet.

Yeah, it's so cheap as to be nearly free.

> Things like massively increased energy cost, strain on the grid

This is a peculiarly USA-localized problem. For a large number of reasons, datacenters are going up all over the world now, and proportionally more of them are outside the US than has been the case historically. And a lot of these places have easier access to cheaper, cleaner power with modernized grids capable of handling it.

> pollution from higher energy use

Somewhat coincidentally as well, energy costs in China and the EU are projected to go down significantly over the the next 10 years due to solar and renewables, where it's not so clear that's going to happen in the US.

As for the rest of the arguments around chip manufacturing and shipping and everything else, well, what do you expect? That we would just stop making chips? We only stopped using horses for transportation when we invented cars. I don't yet see what's going to replace our need for computing.

> it's so cheap as to be nearly free

Both chips and developer time are expensive. Massively so, both in direct cost and secondary and tertiary elements. (If you think hiring more developers to optimise code has no knock-on effects, I have a bridge to sell you.)

There isn't an iron law about developer time being less valuable than chips. When chip progress stagnates, we tend towards optimising. When the developer pipeline is constrained, e.g. when a new frontier opens, we tend towards favouring exploration over optimisation.

If a CS programme is teaching someone to always try to optimise an algorithm versus consider whether hardware might be the limitation, it's not a very good one. In this case, when it comes to AI, there is massive investment going into trying to find more efficient training and inference algorithms. Research which, ironically enough, generally requires access to energy.

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