> A linear fit on a logarithmic y-axis implies exponential growth. The fit on the graph is exponential, which makes it exp(exp(x)) growth. But it is not clear

> A linear fit on a logarithmic y-axis implies exponential growth. The fit on the graph is exponential, which makes it exp(exp(x)) growth. But it is not clear why it should be that, or linear, or logarithmic, which is what I meant. Covid was exponential growth because the rate of infection (assuming a large enough population) is proportional to the current number of actively infected people. What is the analogy here? Is there a similarly widely accepted theory for why the models will improve exponentially rather than linearly? I am not sure recursive self-improvement is that clear to be going on, for instance. I told you which outcomes I talk about. Even in a scenario where models improve exponentially, you still cannot have exponential growth in the long run in the same way that you cannot have that in the covid19 case either: you saturate the population. In the covid case a significant amount of the population has gotten infected so there are less people to infect, in the math case the problems to solve are gonna run out. Then, the bottleneck is how to pose new problems/set new directions of research, which was already not an easy problem to solve. The growth of covid was actually a logistic function, not an exponential one.