Sure. We will probably get enormous hardware progress over the next few decades, so that’s not really an obstacle.
As we get more hardware and slow mostly-aligned AGI/AI progress this further raises the bar for foom.
It seems to me your argument is “smarter than human intelligence cannot make enormous hardware or software progress in a relatively short amount of time”, but this has nothing to do with “efficiency arguments”.
That is actually an efficiency argument, and in my brain efficiency post I discuss multiple sub components of net efficiency that translate into intelligence/$.
The bottleneck is not energy, the bottleneck is algorithmic improvements and improvements to GPU production, neither of which is remotely bottlenecked on energy consumption.
Ahh I see—energy efficiency is tightly coupled to other circuit efficiency metrics as they are all primarily driven by shrinkage. As you increasingly bottom out hardware improvements energy then becomes an increasingly more direct constraint. This is already happening with GPUs where power consumption is roughly doubling with each generation, and could soon dominate operating costs.
See here where I line the roodman model up to future energy usage predictions.
All that being said I do agree that yes the primary bottlneck or crux for the EY fast takeoff/takeover seems to be the amount of slack in software and scaling laws. But only after we agree that there isn’t obvious easy routes for the AGI to bootstrap nanotech assemblers with many OOM greater compute per J than brains or current computers.
As we get more hardware and slow mostly-aligned AGI/AI progress this further raises the bar for foom.
That is actually an efficiency argument, and in my brain efficiency post I discuss multiple sub components of net efficiency that translate into intelligence/$.
Ahh I see—energy efficiency is tightly coupled to other circuit efficiency metrics as they are all primarily driven by shrinkage. As you increasingly bottom out hardware improvements energy then becomes an increasingly more direct constraint. This is already happening with GPUs where power consumption is roughly doubling with each generation, and could soon dominate operating costs.
See here where I line the roodman model up to future energy usage predictions.
All that being said I do agree that yes the primary bottlneck or crux for the EY fast takeoff/takeover seems to be the amount of slack in software and scaling laws. But only after we agree that there isn’t obvious easy routes for the AGI to bootstrap nanotech assemblers with many OOM greater compute per J than brains or current computers.
How much room is there in algorithmic improvements?