[hsuduebc2]

These AI sceptics always omit the possibility that running LLMs could become much cheaper in the future, which it almost certainly will.

This is largely an infrastructure problem, and it will be solved just as similar problems were solved with storage, network capacity, and computing power.

There will be bankruptcies, but the technology will prevail, just as the web did after the dot com bubble. Personally, I see the loss of craft and practical skill, which can only be built through doing, as a much greater danger. I still practise LeetCode from time to time so I do not lose the ability to write code myself.

[pentaphobe]

If it gets cheaper then Jevons Paradox [1] would strongly indicate that it won't in any way reduce the negative impacts (and would tend to actually increase them)

[^1]: https://en.wikipedia.org/wiki/Jevons_paradox

Excerpt: Jevons Paradox/Effect is an economic phenomenon said to occur when technological improvements that increase the efficiency of a resource's use lead to a rise, rather than a fall, in total consumption of that resource

[louwrentius]

This mostly reads like wishful thinking about the future, that's not a basis to make decisions on right now.

[hsuduebc2]

I do not think that wishful thinking is assumption that rapidly evolving hardware which is currently one of the bottlenecks in high demand would become faster in the future.

[louwrentius]

This isn't how it works. Hundreds of billions are spend on existing NVIDIA AI hardware that must first be recuperated. This kind of speculation doesn't make sense to me.

[hsuduebc2]

Of course, that does not mean there will not be significant advances in the future.

A substantial portion of this hardware is also being used for rapid model development rather than simply operating existing models and building a stable revenue stream. These companies are focused primarily on training and improving the models, not merely running them, which is significantly cheaper.