Its not hard to tell at all, just look at how much it costs to run a 10T param model (especially with parallelized agents). Those costs are not worth the occasional slot machine-eque jackpot you get. For an entity like Google it might be worth it, but that's it. They definitely aren't going to let us use these things for cost they are now for much longer.

Imagine going back to 2020 and tell people in 6 years going to be able to spend $200.00 a month and be able to spin up $2mm in GPUs at full throttle to respond to your emails. None of this makes sense.

You don't pay for a £200 a month account to respond to your emails, and if you are, I would tell you that you're wasting your money.

oh, sorry, I'm not running a 10T param. Just local models for me. kk thx.

Whenever you solve any hard problem, you start off by finding a complicated solution, which you then scale down to a simpler solution.

LLMs are a "complicated solution" in the sense that they're expensive. Once you know what they're capable of, you can scale them down to something less expensive. There's usually a way.

Also, an important advantage of LLMs over other approaches is that it's easy to improve them by finding better ways of prompting them. Those prompting strategies can then get hard-coded into the models to make them more efficient. Rinse and repeat. Similarly, you can produce curated data to make them better in certain areas like programming or mathematics.

Do you realize you're fighting a strawman or do you actually think this is a compelling argument?