I think the answer is more subtle than this. The key difference between the current AI automation wave and previous automation waves is that previous automation was done using purpose built specific technologies for each task you want to automate. So for example if you want to automate screwing the lid onto a container you have a special robot with special programming that just screws the lid onto that container. If you have a human role where a person does three things like that, then you start by making a machine to put the two halves of the container together, then fold the item into the container, then a third machine to screw the lid on and the role is automated. That is you deliberately guide the development to automate a whole role rather than randomly building machines to do individual tasks until you happen to automate a role by chance. But because the LLM is a general purpose technology whose performance on any given task is pseudorandom and not controlled by the firm using it (so no specialized datasets usually), what you get instead are threshold effects where the LLM might be able to do 90% of a job but the last 10% still needs to be delegated until the LLM happens to advance to the point where it can do 100% of the job. This means that it won’t necessarily be obvious that a job is about to be automated until models cross that crucial final 5-10% threshold where it can do the entire role rather than just large fractions of the role.
Basically model it as a role or job being made of n tasks, say twenty for a complex role. Each generation the LLM becomes able to do two more tasks in the role on average. The LLM might become suddenly able to do thousands and thousands of new individual tasks on each model release, but it’s kind of a grab bag and not being targeted at automating any particular role (save perhaps AI development). So even though the LLM can do many new things, you still have to wait ten generations of release before it actually automates all 20 tasks in the role and eliminates the job. By contrast older automation would automate a few things at a time but extremely specifically targeted to eliminate roles and reduce headcount.
I think the answer is more subtle than this. The key difference between the current AI automation wave and previous automation waves is that previous automation was done using purpose built specific technologies for each task you want to automate. So for example if you want to automate screwing the lid onto a container you have a special robot with special programming that just screws the lid onto that container. If you have a human role where a person does three things like that, then you start by making a machine to put the two halves of the container together, then fold the item into the container, then a third machine to screw the lid on and the role is automated. That is you deliberately guide the development to automate a whole role rather than randomly building machines to do individual tasks until you happen to automate a role by chance. But because the LLM is a general purpose technology whose performance on any given task is pseudorandom and not controlled by the firm using it (so no specialized datasets usually), what you get instead are threshold effects where the LLM might be able to do 90% of a job but the last 10% still needs to be delegated until the LLM happens to advance to the point where it can do 100% of the job. This means that it won’t necessarily be obvious that a job is about to be automated until models cross that crucial final 5-10% threshold where it can do the entire role rather than just large fractions of the role.
Basically model it as a role or job being made of n tasks, say twenty for a complex role. Each generation the LLM becomes able to do two more tasks in the role on average. The LLM might become suddenly able to do thousands and thousands of new individual tasks on each model release, but it’s kind of a grab bag and not being targeted at automating any particular role (save perhaps AI development). So even though the LLM can do many new things, you still have to wait ten generations of release before it actually automates all 20 tasks in the role and eliminates the job. By contrast older automation would automate a few things at a time but extremely specifically targeted to eliminate roles and reduce headcount.
I agree, that makes a lot of sense.