I tried to replicate your test but after 8 minutes and more than 50 lines of "thinking" by dumping seemingly random loading-screen strings like "Placing the sun, clouds, seagulls, and sea backdrop" and "Positioning the tail feathers and calculating handlebar geometry" I gave up and cancelled the task.
Compare the games your average high-end smartphone can run to the AAA titles of the 2010's. It's not a matter of "unless it is able to" but "when it is able to".
That's going from 150w 720p gaming to ~15w 720p gaming in ~10 years. Let's say an inference cluster draws 1500w to deliver a small-ish 500b model at reasonable speeds/quantization.
Extrapolating from your gaming example, it will take smartphones only... *checks clipboard* ...100 years to achieve datacenter-level performance at the pace of 2010's improvements.
We don't need datacenter-level performance in a handheld device just like we didn't need a storage room in our backyard for a mid-sized, tape-based NAS because technology did its thing and gave us something smarter, smaller and faster.
Your argument is true if the number of parameters in an LLM is the only measurement for quality - a bit like number of bolts per aircraft or lines of code in software. I'd bet you that an 8b parameter LLM will in 5 years outperform the datacenter-level LLMs of today.
This is what people forget when they see slow performance: at 1 t/s it's still roughly the equivalent of having another person work for you at no extra cost besides the initial purchase/sign-on-bonus.
Frontier models are amazing, but what will really be useful for us is having models and hardware so efficient that you can run useful LLMs locally. One of my favourite LLMs to this day is still my jail-broken gemma4 12b because it's small enough to run on my computer, but also 100% local and free as in liberty.
To be fair, humans have it in limited form, too. We just don't know how much comprehension we do not yet have, because we cannot comprehend something we cannot comprehend.
My parrot clearly understands basic events and phrases. He knows what "snacks" involve when I ask if we should have some, he knows the difference between "good morning" and "bedtime", and he can correctly use "Oh!" when he stumbles and follow up with a "Good boy!" when he gets back up again.
But he cannot fathom the complexity of "going to work to earn money".
Just like we humans cannot fathom the complexity of something we have yet to fully understand. People who experience a DMT trip will experience the journey but be unable to comprehend and explain what happened in hindsight. I'm sure there's a TON more we cannot comprehend that we don't know about.
Also we can't comprehend why parrots are not going to parrot work to earn parrot money, or how parrot-to-parrot communication works, or how to be a good and respected parrot in a parrot society.
I also can't comprehend why I wake up early to go to human work to earn human money.
Best I can do is some high-school mumbo-jumbo about farming and specialization furthering wealth acquisition.
But to truly comprehend the situation I'd have to study economics and current events and sociology and even then I think it's a lot of theories and sometimes when I hear economists talk I wonder that it may not be coming out of their mouth.
When I'm bored and Tiktok brain rot isn't enough, I sometimes scroll through LinkedIn to see what happened in my network. The most interesting events to me is "oh, that guy has been at for years now, cool" and "ah that guy got a new job, how nice"... and then I go to bed. :D
When I was a teen I had no experience with software development. Then I went to school and learned about it. Now I'm making money doing this thing because I became quite good.
The world didn't fit around me, so I made "me" fit around "the world". I bet those stubborn dinosaurs can learn a new trick or two also, if management lets them do it during working ours...
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