Thanks for the post. I would like to add that I see a difference in automation speed of cognitive work and physical work. In physical work the growth of productivity is rather constant. With cognitive work there is a sudden jump from not much use cases to a lot of use cases ( like a sgmoid). And physical labour has speed limits. And also costs, generality and deployment are different.
It is very difficult to create a usefull AI for legal or programming work. But once you are over the treshold (as we are now) there are a lot of use cases and productivity growth is very fast. Robotics in car manufacturing took a long time and continued steadily. A few years ago the first real applications of legal AI emerged, and now we have a computer that can pass the bar exam. This time frame is much shorter.
The other difference is speed. A robot building a car is limited in speed. Compare this to a legal AI summarizing legal texts (1000x+ increase in speed). AI doing coginitve work is crazy fast and has the potential to become increasingly faster with more and cheaper compute.
The cost is also different. The marginal cost for robots are higher than for a legal AI. Robots will always be rather narrow and expensive (A Roomba is about as expensive as a laptop). Building one robo lawyer will be very expensive. But after that copying it is very cheap (low marginal costs). Once you are over the treshold, the cost of deployment is very low.
The generality of AI knowledge workers is somewhat of a surprise. It was thought that specialized AI’s would be better, cheaper etc. Maybe a legal AI could be a somewhat finetuned GPT-4. But this model would still be a decent programmer and accountant. A more general AI is much easier to deploy. And there might be unknown use cases for a lawyer, programmer, accountant we have not thought of yet.
Deployment speed is faster for cognitive work and this has implications for growth. When a GPT+1 is introduced all models are easily replaced by the better and faster model. When you invent a better robot to manufacture cars it will take decades before this is implemented in every factory. But the changing the the base model of your legal AI from gpt4 to gpt5 might be just a software update.
In summary there are differences for automating cognitive work with regard to:
growth path (sigmoid instead of linear)
speed of excecuting work
cost (low marginal cost)
generality (the robo lawyer, programmer, accountant)
deployment speed (just a software update)
Are there more differences that effect speed? Am I being too bullish?
Thanks for the post. I would like to add that I see a difference in automation speed of cognitive work and physical work. In physical work the growth of productivity is rather constant. With cognitive work there is a sudden jump from not much use cases to a lot of use cases ( like a sgmoid). And physical labour has speed limits. And also costs, generality and deployment are different.
It is very difficult to create a usefull AI for legal or programming work. But once you are over the treshold (as we are now) there are a lot of use cases and productivity growth is very fast. Robotics in car manufacturing took a long time and continued steadily. A few years ago the first real applications of legal AI emerged, and now we have a computer that can pass the bar exam. This time frame is much shorter.
The other difference is speed. A robot building a car is limited in speed. Compare this to a legal AI summarizing legal texts (1000x+ increase in speed). AI doing coginitve work is crazy fast and has the potential to become increasingly faster with more and cheaper compute.
The cost is also different. The marginal cost for robots are higher than for a legal AI. Robots will always be rather narrow and expensive (A Roomba is about as expensive as a laptop). Building one robo lawyer will be very expensive. But after that copying it is very cheap (low marginal costs). Once you are over the treshold, the cost of deployment is very low.
The generality of AI knowledge workers is somewhat of a surprise. It was thought that specialized AI’s would be better, cheaper etc. Maybe a legal AI could be a somewhat finetuned GPT-4. But this model would still be a decent programmer and accountant. A more general AI is much easier to deploy. And there might be unknown use cases for a lawyer, programmer, accountant we have not thought of yet.
Deployment speed is faster for cognitive work and this has implications for growth. When a GPT+1 is introduced all models are easily replaced by the better and faster model. When you invent a better robot to manufacture cars it will take decades before this is implemented in every factory. But the changing the the base model of your legal AI from gpt4 to gpt5 might be just a software update.
In summary there are differences for automating cognitive work with regard to:
growth path (sigmoid instead of linear)
speed of excecuting work
cost (low marginal cost)
generality (the robo lawyer, programmer, accountant)
deployment speed (just a software update)
Are there more differences that effect speed? Am I being too bullish?