[naasking]

> is that AGI is too resource-intensive to be within the reach of normal computing of the ordinary user for at least 2 decades.

Hardware is still accelerating exponentially in density, albeit a bit slower. What you're not considering is that algorithmic improvements in machine learning are outpacing hardware improvements.

For instance, NVidia recently revealed how to switch from 32-bit floats to 16-bit floats with no perceptible loss in effectiveness, and they're working on 8-bit floats next. That's a full doubling in number of parameters in your model in only a single step. Other improvements are refinements to language models themselves to reduce overfitting and boost effectiveness with fewer parameters.

Arguably a machine learning model will achieve parity with human neuron density, in terms of number parameters, within the next decade. What that actually means is unclear.