I'd say there's a mix of 'Chinese GPUs are not that good after all' and 'Nvidia doesn't have any magical secret sauce, and China could easily catch up' going on. Nvidia GPUs are indeed remarkable devices with a complex software stack that offers all kinds of possibilities that you cannot replicate over night (or over a year or two!)

However they've also got a fair amount of generality, anything you might want to do that involves huge amounts of matmuls and vector maths you can probably map to a GPU and do a half decent job of it. This is good for things like model research and exploration of training methods.

Once this is all developed you can cherry pick a few specific things to be good at and build your own GPU concentrating on making those specific things work well (such as inference and training on Transformer architectures) and catch up to Nvidia on those aspects even if you cannot beat or match a GPU on every possible task, however you don't care as you only want to do some specific things well.

This is still hard and model architectures and training approaches are continuously evolving. Simplify things too much and target some ultra specific things and you end up with some pretty useless hardware that won't allow you to develop next year's models, nor run this year's particularly well. You can just develop and run last year's models. So you need to hit a sweet spot between enough flexibility to keep up with developments but don't add so much you have to totally replicate what Nvidia have done.

Ultimately the 'secret sauce' is just years of development producing a very capable architecture that offers huge flexibility across differing workloads. You can short-cut that development by reducing flexibility or not caring your architecture is rubbish at certain things (hence no magical secret sauce). This is still hard and your first gen could suck quite a lot (hence not that good after all) but when you've got a strong desire for an alternative hardware source you can probably put up with a lot of short-term pain for the long-term pay off.

What does "are not good after all" even mean? I feel there are too many value judgements in that question's tone, that blindsides western observers. I feel like the tone has the hidden implication of "this must be fake after all, they're only good at faking/stealing, nothing to see here move along".

Are they as good as Nvidia? No. News reporters have a tendency to hype things up beyond reality. No surprises there.

Are they useless garbage? No.

Can the quality issues be overcome with time and R&D? Yes.

Is being "worse" a necessary interim step to become "good"? Yes.

Are they motivated to become "good"? Yes.

Do they have a market that is willing to wait for them to become "good"? Also yes. It used to be no, but the US created this market for them.

Also, comparing Chinese AI chips to Nvidia is a bit like comparing AWS with Azure. Overcoming compatibility problems is not trivial, you can't just lift and shift your workload to another public cloud, you are best off redesigning your entire infra for the capabilities of the target cloud.