I doubt you are going to change anyone's mind on this who is already convinced that computers can think and reason and all that's required is the right sequence of numbers. Moreover, the internet is awash with bots and AIs working on behalf of governments to spread political and economic propaganda. HN has moderation to avoid it but the moderators are human so they can't foil all attempts and notice all AIs and bots.
Most of what you've said is true but what exactly does it mean to "break barriers"? We can not escape the laws of chemistry, physics, and thermodynamics because we live on a compact manifold with finite resources which must be recycled eventually by the surrounding ecology. This is why plastics are now found in all newborns, the chemicals produced by our factories are recycled back into the ecology and our internal biomes.
Like most things in software the use cases are the limits of one's imagination. The browser has always been a Turing complete development environment so this is just another demonstration.
Neural networks are Turing complete, i.e. there is a universal neural network that can compute any effectively computable function¹. Incidentally, when this is combined with Rice's theorem² it means that safety research is essentially an unsolvable problem because any non-trivial property of a sufficiently complex neural network, e.g. one that can simulate a Turing machine, will have properties which can not be predicted with finite computation.
I thought the whole point of neural networks was that they were good at searching through these spaces. I'm pretty sure OpenAI is pruning their models behind the scenes to reduce their costs because that's the only way they can keep reducing the cost per token. So their secret sauce at this point is whatever pruning AI they're using to whittle the large computation graphs into more cost efficient consumer products.
He was right that scaling would not achieve abstract reasoning and he's been right so far on basically every new hyped development. The closed research labs like OpenAI are hoping to reduce all the gaps in their models by constantly patching out of distribution data sets but this clearly can not achieve any sort of general intelligence unless they somehow manage to obsolete themselves and the entire company by automating the out of distribution patching itself which they now perform by burning lots of cash and energy.
There are people who need to write a lot of emails so for those people I'm sure OpenAI will continue to deliver some kind of value but everyone else will still have to continue thinking for themselves regardless of what Sam Altman keeps promising.
These algorithms are not capable of symbolic reasoning and abstract interpretation. The most obvious demonstration of this is that no algorithm on the market currently can solve sudoku puzzles even though they have spent billions of dollars "training" them on logic and reasoning.
I don't have any subscriptions to the latest models but what improvements have you noticed in scaling and language understanding? Last I checked people were still discussing "9.9 > 9.1" and "How many 'r's are in 'strawberry'".