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This keeps happening to all the people (including many here on The Motte) who are all about theoretical computation and don't have the slightest clue about just how intricate and deep reaching the required manufacturing and design ecosystem is for all the required hardware. Nvidia and other such AI specific IC makers are just the small visible top of the iceberg. An AI "reaching recursive self improvement" won't do jack shit when it can't control the millions of people who are handling everything else. Likewise absolutely massive part of AI improvement is the training on real world data instead of just some abstract compute and without that data (provided directly or indirectly by humans), there is no improvement, because the AI is just playing in the tiny sandbox.
Not to mention the fact that incorporating new data into the models is a pipeline that takes three to six months as soon as it goes beyond trivial database lookup.
The risk is that giant fields of GPUs and ungodly amounts of data are not known to be inherent requirements for learning. As an existence proof, human brains are far more sample efficient than current architectures. Could there be some algorithm cheaply implemented on existing hardware that takes advantage of whatever learning mechanism the human brain uses? It's unclear but plausible. And discovering that may just be a matter of throwing lots of compute at the problem. A superintelligence that can run on an RTX 5090 in a homelab is a very different threat that is much harder to contain.
I'm somewhat sympathetic to the critique that human brains may be architecturally superior to synchronous, digital GPUs in some critical ways, meaning no software singularity. But I wouldn't bet the world on it.
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