This is a common misconception. GPT-3 was trained using a 300B token (~300gb) subset of common-crawl and friends. The model is larger than the dataset.
You could argue despite the huge number of physical degrees of freedom, the operations on DNA can be reduced to copy, repair, express, suppress. On the other hand, there's still a ton of intrinsic complexity in storing a huge amount of data, and yeah some nucleotides are totally essential.
The other thing a wonder about: sure, maintaining a proteome is hugely complicated, but how much of this complexity goes into maintaining homeostasis (e.g. metabolism, cytoskeloton and membrane maintenance, replication,...) vs. enabling computation. Seems like silicon has the advantage here.
My 2c (apologies for the aggressive tone -- I'm just excited about AGI):
That's a very very weak upper bound on how much hardware it takes. I think it's not all that different from emulating a Nintendo64 with a quantum simulation of the hardware.
For complex systems to work (not to mention evolve), they need to be robust to small perturbations -- there's no way the computation the brain is doing is sensitive to the details of particular atoms. There has to be redundancy, modularity, etc. These things aren't human inventions so much as they are the only way to meaningfully move in a 2^|giant-number| state-space.