I agree. Humans indeed uncover “depths of meaning” from data that seem to be inaccessible to machines. In researcher-speak, it’s called data efficiency: Humans attain useful intelligence in only ~200M words, many OOMs faster than machines. This difference between humans and machines isn’t necessarily a problem economically, because the high cost of training an economically useful machine can be amortized over many cheaply cloned inference workers. But at a planetary scale, it could become a problem in terms of population dynamics or memetic entropy. Large clonal swarms of insects are known to be vulnerable to viral collapse. A global population of intelligences which lack the human “regularization stack” — individuality and mortality — could yield unstable or inefficient global dynamics, similar to training an MoE neural network with poor load balancing. Rather than being a matter of “good vs evil,” placing some limitations on the proliferation of machine intelligence could be necessary simply in order to prevent global “loss spikes.”