But seriously, video encoding isn't AI. Video encoding is a well understood problem. We can't even make "AI" that doesn't hallucinate yet. We're not sure what architectures will be needed for progress in AI. I get that we're all drunk on our analogies in the vacuum of our ignorance but we need to have a bit of humility and awareness of where we're at.
Including considering that it can't be made much better, that the hallucinations are a fundamental trait that cannot be eliminated, that this will all come tumbling down in a year or three. You seem to want to consider every possible positive future if we just work harder or longer at it, while ignoring the most likely outcomes that are nearer term and far from positive.
Conversely, can you name one computing thing that used to be hard when it was first created that is still hard in the same way today after generations of software/hardware improvements?
Simulations and pretty much any large scale modelling task. Why do you think people build supercomputers?
Now that I mentioned it, I think supercomputers and the jobs they run are the perfect analog for AI at this stage. It's a problem that we could throw nearly limitless compute at if it were cost effective to do so. HPC encompasses a class of problems for which we have to make compromises because we can't begin to compute the ideal(sort of like using reduced precision in deep-learning). HPC scale problems have always been hard and as we add capabilities we will likely just soak them up to perform more accurate or larger computational tasks.
To quote Andrej Karpathy
(https://x.com/karpathy/status/1883941452738355376): "I will say that Deep Learning has a legendary ravenous appetite for compute, like no other algorithm that has ever been developed in AI. You may not always be utilizing it fully but I would never bet against compute as the upper bound for achievable intelligence in the long run. Not just for an individual final training run, but also for the entire innovation / experimentation engine that silently underlies all the algorithmic innovations."
VR is as dead this time around as it was in the mid-2000s and the mid-1990s and the mid-1980s, each of the times I've used it it was just as awful as before with nausea, eyestrain, headaches, neck and face fatigue, it's truly a f**ed space and it's failed over and over, this time with Apple and Facebook spending tens of billions on it. VR is a perfect reply to your question here.
He fixes the cable?
But seriously, video encoding isn't AI. Video encoding is a well understood problem. We can't even make "AI" that doesn't hallucinate yet. We're not sure what architectures will be needed for progress in AI. I get that we're all drunk on our analogies in the vacuum of our ignorance but we need to have a bit of humility and awareness of where we're at.