Amdahl's Law and AI Productivity
Amdahl’s law, from 1967, perfectly describes everything about AI productivity. It explains why some people think productivity is way up and some see negligible impact. What is Amdahl’s law? Amdahl’s law is most often used around parallelizing code. It says that the gain from optimizing code depends on the portion that you can’t optimize, or more general:
“the overall performance improvement gained by optimizing a single part of a system is limited by the fraction of time that the improved part is actually used”
Amdahl’s law is the reason why the German railroad no longer aims for speeds above 300km/h and more or less settled on 250-300km/h. Because the German railroad system is limited by the many stops. The fastest train in Germany from Berlin to Frankfurt (Sprinter) is not so fast because it goes so fast, but because it does not stop. And if you can’t remove the stops, there is no need to go faster. The gain gets less and less but the costs rise and rise.
Amdahl’s law is core to the understanding of AI productivity. Your productivity gain is determined by the time you spend with AI and the parts of your work that you can’t (yet) use AI for. When I’ve started coding with Cursor, my idea->code part doubled or tripled in speed. I want something, I tell the AI, then <tab><tab><tab> - done.
