This isn't accurate. GPT-3 has 175B parameters. The human brain has ~175B cells (neurons, glia, etc.) The analog to GPT-3's parameter count would be synapses, not neurons, where even conservative estimates put the human brain at several orders of magnitude larger. It's likely that >90% of the 175B could be pruned with little change in performance. That changes the synapse ratios since we know the brain is quite a bit sparser. In addition, the training dataset is likely broader than the majority of Internet users. Basically, its not an apples-to-apples comparison.
That said, I agree that simply scaling model and data is the naive approach.
If you can get GPT-like performance out of a 17B model, you should publish that.
Retrieval models (again, lots of published examples: RETRO, etc.) that externalize their data will bring the sizes down by about that order as well.
We do on the other hand know for a fact it's possible to run an instance of consciousness in a volume of about a liter that consumes like 20 watts (aka your average human brain), so there's something probably wrong with our general approach to the matter. GPT-3 already uses about twice as many parameters as our organic counterparts do, with much worse results. And it even doesn't have to process a ridiculously large stream of sensor data and run an entire body of muscle actuators at the same time.