Robin’s economic model for growth explosion with AI uses a continuum of tasks for automation. The idea is that as you automate more tasks, those tasks are done very efficiently, but the remaining ones become bottlenecks, making up more of GDP and limiting growth. Think Baumol’s cost disease: as our manufacturing productivity has increased, economic growth winds up more limited by productivity improvements in service sectors like health care and education, computer programming, and science that have been resistant to automation.
As you eliminate the last bottlenecks where human labor is important, you can speed the whole process of growth up to computer-scales, rather than being held back by the human weakest link. One can make an analogy to Amdahl’s law: if you can parallelize 50% of a problem you can double your speed, at 90% you can get a 10x speedup, at 99% a 100x speedup, and as you approach 100% you can rush up to other limits.
Similarly, smooth human progress in automating elements of AI development (which then proceed very quickly, bottlenecked by the human-limited elements) could produce stark speedup as automation approaches 100%.
However, such developments do make a world dominated by whole brain emulations rather than neuromorphic AI less likely, even if they still allow an intelligence explosion.
Robin’s economic model for growth explosion with AI uses a continuum of tasks for automation. The idea is that as you automate more tasks, those tasks are done very efficiently, but the remaining ones become bottlenecks, making up more of GDP and limiting growth. Think Baumol’s cost disease: as our manufacturing productivity has increased, economic growth winds up more limited by productivity improvements in service sectors like health care and education, computer programming, and science that have been resistant to automation.
As you eliminate the last bottlenecks where human labor is important, you can speed the whole process of growth up to computer-scales, rather than being held back by the human weakest link. One can make an analogy to Amdahl’s law: if you can parallelize 50% of a problem you can double your speed, at 90% you can get a 10x speedup, at 99% a 100x speedup, and as you approach 100% you can rush up to other limits.
Similarly, smooth human progress in automating elements of AI development (which then proceed very quickly, bottlenecked by the human-limited elements) could produce stark speedup as automation approaches 100%.
However, such developments do make a world dominated by whole brain emulations rather than neuromorphic AI less likely, even if they still allow an intelligence explosion.