Earlier quoted context omitted.
The thing is though, once we have a benchmark that we pass, it’s pretty typical to be able to bring down time required in short order through performance improvements and iterating on ideas. So if you knew you had GAI but it took 100% of all GCP for 3 years to give a result, within the next 5 years that would come down significantly (not least of which you’d build HW dedicated to accelerating the slow parts).
That's patently false for many classes of problems. We know exactly how to solve the traveling salesman problem, and have for decades, but we're nowhere close to solving a random 1000 city case (note: there are approximate methods that can find good, but not optimal, results on millions of cities). Edit: I should say 1,000,000 city problem, as there are some solutions for 30-60k cities from the 2000s. And there are g…
While I agree that not all problems show this kind of acceleration in performance, that's typically only true if you've already spent so much time trying to solve it that you've asymptoted to the optimal solution. Right now we're nowhere near the asymptote for AI improvements. Additionally, there's so many research dollars flowing into AI precisely because the potential upside here is nowhere near realized and there's lots of research lines still left to be explored. George Hinton ended the AI winter.